Learning compositional sequences with multiple time scales through a hierarchical network of spiking neurons
Fig 5
Measuring variability and performance in the read-out dynamics.
A. The time between motifs 1 and 2 and motifs 2 and 3 is measured during spontaneous dynamics. We plot the coefficient of variation of these times (50 trials) on the y-axis, for the three target sequences in Fig 4A. B. The cross correlation between the spike trains in the first motif and the second and third motif is measured, normalized by the auto-correlation of the spike trains in motif 1. The maximum of the cross correlation is recorded in each trial (50 trials). This is repeated for the three target sequences in Fig 4A. C. We measure the error between the target sequence with 25 ms stimulations in Fig 4A and spike trains in motif 1, 2 and 3. In both models, the performance degrades towards later occurring motifs. The degradation is significantly worse in the serial model: a linear regression yields a slope of 0.0163 for the serial model and a slope of 0.0048 for the hierarchical model (p < 10−5 using t-test). D. The serial clock (48 clusters) is obtained by adding the slow (28 clusters) and fast (20 clusters) clocks together. Sequential dynamics is simulated 50 times for each clock. The time at which each cluster is activated in the sequential dynamics is measured. The standard deviation of these activation times is plotted as a function of the cluster index. The serial clock has a maximal variability of about 9 ms. The fast and slow clock have a maximal variability of about 3 and 35 ms respectively.